Overview

Dataset statistics

Number of variables39
Number of observations150080
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.9 MiB
Average record size in memory300.0 B

Variable types

Numeric6
Categorical32
Unsupported1

Warnings

model_name has a high cardinality: 544 distinct values High cardinality
options_airbag-driver is highly correlated with options_airbag-passengerHigh correlation
options_airbag-passenger is highly correlated with options_airbag-driverHigh correlation
options_airbag-driver is highly correlated with options_airbag-passengerHigh correlation
vendor is highly correlated with brandHigh correlation
options_airbag-passenger is highly correlated with options_airbag-driverHigh correlation
brand is highly correlated with vendorHigh correlation
mileage is an unsupported type, check if it needs cleaning or further analysis Unsupported
price has 34686 (23.1%) zeros Zeros

Reproduction

Analysis started2021-05-03 12:34:35.896319
Analysis finished2021-05-03 12:37:32.290271
Duration2 minutes and 56.39 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

Distinct115579
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49121.84832
Minimum0
Maximum118284
Zeros2
Zeros (%)< 0.1%
Memory size1.1 MiB
2021-05-03T15:37:32.579098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3764
Q118847
median40552.5
Q379317.25
95-th percentile110762.05
Maximum118284
Range118284
Interquartile range (IQR)60470.25

Descriptive statistics

Standard deviation35080.41437
Coefficient of variation (CV)0.7141509445
Kurtosis-1.130914595
Mean49121.84832
Median Absolute Deviation (MAD)27063
Skewness0.4162004983
Sum7372206996
Variance1230635473
MonotocityNot monotonic
2021-05-03T15:37:32.913831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02
 
< 0.1%
230932
 
< 0.1%
45402
 
< 0.1%
65892
 
< 0.1%
4462
 
< 0.1%
24952
 
< 0.1%
333262
 
< 0.1%
292322
 
< 0.1%
312812
 
< 0.1%
251382
 
< 0.1%
Other values (115569)150060
> 99.9%
ValueCountFrequency (%)
02
< 0.1%
12
< 0.1%
22
< 0.1%
32
< 0.1%
42
< 0.1%
ValueCountFrequency (%)
1182841
< 0.1%
1182831
< 0.1%
1182821
< 0.1%
1182811
< 0.1%
1182791
< 0.1%

bodyType
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
внедорожник 5 дв.
68464 
седан
42722 
хэтчбек 5 дв.
7346 
лифтбек
7000 
минивэн
 
6263
Other values (11)
18285 

Length

Max length20
Median length13
Mean length11.76930304
Min length4

Characters and Unicode

Total characters1766337
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowвнедорожник 5 дв.
2nd rowредкий
3rd rowхэтчбек 5 дв.
4th rowвнедорожник 5 дв.
5th rowседан
ValueCountFrequency (%)
внедорожник 5 дв.68464
45.6%
седан42722
28.5%
хэтчбек 5 дв.7346
 
4.9%
лифтбек7000
 
4.7%
минивэн6263
 
4.2%
компактвэн5015
 
3.3%
купе4203
 
2.8%
универсал 5 дв.4119
 
2.7%
пикап двойная кабина2082
 
1.4%
хэтчбек 3 дв.1057
 
0.7%
Other values (6)1809
 
1.2%
2021-05-03T15:37:33.580888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
дв81279
25.7%
579929
25.2%
внедорожник68757
21.7%
седан42722
13.5%
хэтчбек8403
 
2.7%
лифтбек7000
 
2.2%
минивэн6263
 
2.0%
компактвэн5015
 
1.6%
купе4203
 
1.3%
универсал4119
 
1.3%
Other values (9)9112
 
2.9%

Most occurring characters

ValueCountFrequency (%)
н206910
11.7%
д195319
11.1%
в167515
9.5%
166722
9.4%
о146052
 
8.3%
е135870
 
7.7%
к103085
 
5.8%
и96828
 
5.5%
.81279
 
4.6%
579929
 
4.5%
Other values (19)386828
21.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1436791
81.3%
Space Separator166722
 
9.4%
Decimal Number81279
 
4.6%
Other Punctuation81279
 
4.6%
Dash Punctuation266
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
н206910
14.4%
д195319
13.6%
в167515
11.7%
о146052
10.2%
е135870
9.5%
к103085
7.2%
и96828
6.7%
р74530
 
5.2%
ж68757
 
4.8%
а60637
 
4.2%
Other values (14)181288
12.6%
ValueCountFrequency (%)
579929
98.3%
31350
 
1.7%
ValueCountFrequency (%)
166722
100.0%
ValueCountFrequency (%)
.81279
100.0%
ValueCountFrequency (%)
-266
100.0%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic1436791
81.3%
Common329546
 
18.7%

Most frequent character per script

ValueCountFrequency (%)
н206910
14.4%
д195319
13.6%
в167515
11.7%
о146052
10.2%
е135870
9.5%
к103085
7.2%
и96828
6.7%
р74530
 
5.2%
ж68757
 
4.8%
а60637
 
4.2%
Other values (14)181288
12.6%
ValueCountFrequency (%)
166722
50.6%
.81279
24.7%
579929
24.3%
31350
 
0.4%
-266
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic1436791
81.3%
ASCII329546
 
18.7%

Most frequent character per block

ValueCountFrequency (%)
н206910
14.4%
д195319
13.6%
в167515
11.7%
о146052
10.2%
е135870
9.5%
к103085
7.2%
и96828
6.7%
р74530
 
5.2%
ж68757
 
4.8%
а60637
 
4.2%
Other values (14)181288
12.6%
ValueCountFrequency (%)
166722
50.6%
.81279
24.7%
579929
24.3%
31350
 
0.4%
-266
 
0.1%

brand
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
VOLKSWAGEN
28706 
TOYOTA
24906 
BMW
18582 
NISSAN
18118 
MERCEDES
16723 
Other values (7)
43045 

Length

Max length10
Median length6
Mean length6.724860075
Min length3

Characters and Unicode

Total characters1009267
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMITSUBISHI
2nd rowMITSUBISHI
3rd rowMITSUBISHI
4th rowMITSUBISHI
5th rowMITSUBISHI
ValueCountFrequency (%)
VOLKSWAGEN28706
19.1%
TOYOTA24906
16.6%
BMW18582
12.4%
NISSAN18118
12.1%
MERCEDES16723
11.1%
MITSUBISHI11090
 
7.4%
SKODA9641
 
6.4%
AUDI6369
 
4.2%
HONDA6110
 
4.1%
VOLVO4005
 
2.7%
Other values (2)5830
 
3.9%
2021-05-03T15:37:34.201697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
volkswagen28706
19.1%
toyota24906
16.6%
bmw18582
12.4%
nissan18118
12.1%
mercedes16723
11.1%
mitsubishi11090
 
7.4%
skoda9641
 
6.4%
audi6369
 
4.2%
honda6110
 
4.1%
volvo4005
 
2.7%
Other values (2)5830
 
3.9%

Most occurring characters

ValueCountFrequency (%)
S115907
11.5%
O102279
 
10.1%
A93850
 
9.3%
E81296
 
8.1%
N77870
 
7.7%
I71393
 
7.1%
T64311
 
6.4%
W47288
 
4.7%
M46395
 
4.6%
D38843
 
3.8%
Other values (12)269835
26.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1009267
100.0%

Most frequent character per category

ValueCountFrequency (%)
S115907
11.5%
O102279
 
10.1%
A93850
 
9.3%
E81296
 
8.1%
N77870
 
7.7%
I71393
 
7.1%
T64311
 
6.4%
W47288
 
4.7%
M46395
 
4.6%
D38843
 
3.8%
Other values (12)269835
26.7%

Most occurring scripts

ValueCountFrequency (%)
Latin1009267
100.0%

Most frequent character per script

ValueCountFrequency (%)
S115907
11.5%
O102279
 
10.1%
A93850
 
9.3%
E81296
 
8.1%
N77870
 
7.7%
I71393
 
7.1%
T64311
 
6.4%
W47288
 
4.7%
M46395
 
4.6%
D38843
 
3.8%
Other values (12)269835
26.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1009267
100.0%

Most frequent character per block

ValueCountFrequency (%)
S115907
11.5%
O102279
 
10.1%
A93850
 
9.3%
E81296
 
8.1%
N77870
 
7.7%
I71393
 
7.1%
T64311
 
6.4%
W47288
 
4.7%
M46395
 
4.6%
D38843
 
3.8%
Other values (12)269835
26.7%

color
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
чёрный
48716 
белый
28124 
серый
20411 
серебристый
15370 
синий
12881 
Other values (11)
24578 

Length

Max length11
Median length6
Mean length6.501246002
Min length5

Characters and Unicode

Total characters975707
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowбелый
2nd rowбелый
3rd rowзелёный
4th rowчёрный
5th rowкрасный
ValueCountFrequency (%)
чёрный48716
32.5%
белый28124
18.7%
серый20411
13.6%
серебристый15370
 
10.2%
синий12881
 
8.6%
коричневый9908
 
6.6%
красный5333
 
3.6%
зелёный3172
 
2.1%
бежевый2232
 
1.5%
пурпурный1306
 
0.9%
Other values (6)2627
 
1.8%
2021-05-03T15:37:34.823975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
чёрный48716
32.5%
белый28124
18.7%
серый20411
13.6%
серебристый15370
 
10.2%
синий12881
 
8.6%
коричневый9908
 
6.6%
красный5333
 
3.6%
зелёный3172
 
2.1%
бежевый2232
 
1.5%
пурпурный1306
 
0.9%
Other values (6)2627
 
1.8%

Most occurring characters

ValueCountFrequency (%)
й150080
15.4%
ы136295
14.0%
р118040
12.1%
е97547
10.0%
н81617
8.4%
с69858
7.2%
ч58624
 
6.0%
ё52371
 
5.4%
и51960
 
5.3%
б46630
 
4.8%
Other values (12)112685
11.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter975707
100.0%

Most frequent character per category

ValueCountFrequency (%)
й150080
15.4%
ы136295
14.0%
р118040
12.1%
е97547
10.0%
н81617
8.4%
с69858
7.2%
ч58624
 
6.0%
ё52371
 
5.4%
и51960
 
5.3%
б46630
 
4.8%
Other values (12)112685
11.5%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic975707
100.0%

Most frequent character per script

ValueCountFrequency (%)
й150080
15.4%
ы136295
14.0%
р118040
12.1%
е97547
10.0%
н81617
8.4%
с69858
7.2%
ч58624
 
6.0%
ё52371
 
5.4%
и51960
 
5.3%
б46630
 
4.8%
Other values (12)112685
11.5%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic975707
100.0%

Most frequent character per block

ValueCountFrequency (%)
й150080
15.4%
ы136295
14.0%
р118040
12.1%
е97547
10.0%
н81617
8.4%
с69858
7.2%
ч58624
 
6.0%
ё52371
 
5.4%
и51960
 
5.3%
б46630
 
4.8%
Other values (12)112685
11.5%

engineDisplacement
Real number (ℝ≥0)

Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.882480677
Minimum0.7
Maximum408
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-05-03T15:37:35.145822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.4
Q11.8
median2
Q33
95-th percentile4.5
Maximum408
Range407.3
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation8.308460265
Coefficient of variation (CV)2.882399293
Kurtosis443.2354931
Mean2.882480677
Median Absolute Deviation (MAD)0.4
Skewness19.44006955
Sum432602.7
Variance69.03051197
MonotocityNot monotonic
2021-05-03T15:37:35.472799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240156
26.8%
318488
12.3%
1.616533
11.0%
1.89859
 
6.6%
2.59535
 
6.4%
1.47033
 
4.7%
3.56435
 
4.3%
2.46180
 
4.1%
1.24082
 
2.7%
4.52919
 
1.9%
Other values (54)28860
19.2%
ValueCountFrequency (%)
0.7144
 
0.1%
198
 
0.1%
1.18
 
< 0.1%
1.24082
2.7%
1.31543
 
1.0%
ValueCountFrequency (%)
4084
 
< 0.1%
3131
 
< 0.1%
1842
 
< 0.1%
17011
 
< 0.1%
150398
0.3%

enginePower
Real number (ℝ≥0)

Distinct329
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.7627066
Minimum30
Maximum639
Zeros0
Zeros (%)0.0%
Memory size586.4 KiB
2021-05-03T15:37:35.841755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile98
Q1140
median171
Q3235
95-th percentile381
Maximum639
Range609
Interquartile range (IQR)95

Descriptive statistics

Standard deviation91.06185735
Coefficient of variation (CV)0.4724039155
Kurtosis4.71906372
Mean192.7627066
Median Absolute Deviation (MAD)41
Skewness1.878247625
Sum28929827
Variance8292.261864
MonotocityNot monotonic
2021-05-03T15:37:36.148007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1508161
 
5.4%
2497810
 
5.2%
1055542
 
3.7%
1525502
 
3.7%
1905210
 
3.5%
1704832
 
3.2%
1024703
 
3.1%
1804056
 
2.7%
1103937
 
2.6%
1843711
 
2.5%
Other values (319)96616
64.4%
ValueCountFrequency (%)
304
< 0.1%
321
 
< 0.1%
381
 
< 0.1%
401
 
< 0.1%
421
 
< 0.1%
ValueCountFrequency (%)
6398
 
< 0.1%
6302
 
< 0.1%
6261
 
< 0.1%
625324
0.2%
612407
0.3%

fuelType
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
бензин
111023 
дизель
36349 
газ
 
1110
гибрид
 
1082
электро
 
516

Length

Max length7
Median length6
Mean length5.98125
Min length3

Characters and Unicode

Total characters897666
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowбензин
2nd rowэлектро
3rd rowбензин
4th rowбензин
5th rowбензин
ValueCountFrequency (%)
бензин111023
74.0%
дизель36349
 
24.2%
газ1110
 
0.7%
гибрид1082
 
0.7%
электро516
 
0.3%
2021-05-03T15:37:36.775588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:36.999992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
бензин111023
74.0%
дизель36349
 
24.2%
газ1110
 
0.7%
гибрид1082
 
0.7%
электро516
 
0.3%

Most occurring characters

ValueCountFrequency (%)
н222046
24.7%
и149536
16.7%
з148482
16.5%
е147888
16.5%
б112105
12.5%
д37431
 
4.2%
л36865
 
4.1%
ь36349
 
4.0%
г2192
 
0.2%
р1598
 
0.2%
Other values (5)3174
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter897666
100.0%

Most frequent character per category

ValueCountFrequency (%)
н222046
24.7%
и149536
16.7%
з148482
16.5%
е147888
16.5%
б112105
12.5%
д37431
 
4.2%
л36865
 
4.1%
ь36349
 
4.0%
г2192
 
0.2%
р1598
 
0.2%
Other values (5)3174
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic897666
100.0%

Most frequent character per script

ValueCountFrequency (%)
н222046
24.7%
и149536
16.7%
з148482
16.5%
е147888
16.5%
б112105
12.5%
д37431
 
4.2%
л36865
 
4.1%
ь36349
 
4.0%
г2192
 
0.2%
р1598
 
0.2%
Other values (5)3174
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic897666
100.0%

Most frequent character per block

ValueCountFrequency (%)
н222046
24.7%
и149536
16.7%
з148482
16.5%
е147888
16.5%
б112105
12.5%
д37431
 
4.2%
л36865
 
4.1%
ь36349
 
4.0%
г2192
 
0.2%
р1598
 
0.2%
Other values (5)3174
 
0.4%

mileage
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size1.1 MiB

model_name
Categorical

HIGH CARDINALITY

Distinct544
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
x_trail
 
4928
passat
 
4534
land_cruiser
 
4398
tiguan
 
4241
land_cruiser_prado
 
4003
Other values (539)
127976 

Length

Max length20
Median length6
Mean length6.256589819
Min length1

Characters and Unicode

Total characters938989
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)< 0.1%

Sample

1st rowoutlander
2nd rowminicab
3rd rowlancer
4th rowpajero
5th rowlancer_evolution
ValueCountFrequency (%)
x_trail4928
 
3.3%
passat4534
 
3.0%
land_cruiser4398
 
2.9%
tiguan4241
 
2.8%
land_cruiser_prado4003
 
2.7%
octavia3843
 
2.6%
caddy3696
 
2.5%
polo3150
 
2.1%
5er3072
 
2.0%
3er2900
 
1.9%
Other values (534)111315
74.2%
2021-05-03T15:37:37.734879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
x_trail4928
 
3.3%
passat4534
 
3.0%
land_cruiser4398
 
2.9%
tiguan4241
 
2.8%
land_cruiser_prado4003
 
2.7%
octavia3843
 
2.6%
caddy3696
 
2.5%
polo3150
 
2.1%
5er3072
 
2.0%
3er2900
 
1.9%
Other values (534)111315
74.2%

Most occurring characters

ValueCountFrequency (%)
a127983
13.6%
r81204
 
8.6%
e73094
 
7.8%
s71948
 
7.7%
l61490
 
6.5%
_50384
 
5.4%
t45583
 
4.9%
c45238
 
4.8%
i43851
 
4.7%
o41405
 
4.4%
Other values (27)296809
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter845344
90.0%
Connector Punctuation50384
 
5.4%
Decimal Number43261
 
4.6%

Most frequent character per category

ValueCountFrequency (%)
a127983
15.1%
r81204
 
9.6%
e73094
 
8.6%
s71948
 
8.5%
l61490
 
7.3%
t45583
 
5.4%
c45238
 
5.4%
i43851
 
5.2%
o41405
 
4.9%
n35168
 
4.2%
Other values (16)218380
25.8%
ValueCountFrequency (%)
07714
17.8%
57696
17.8%
65887
13.6%
35878
13.6%
45862
13.6%
73529
8.2%
82275
 
5.3%
12083
 
4.8%
21232
 
2.8%
91105
 
2.6%
ValueCountFrequency (%)
_50384
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin845344
90.0%
Common93645
 
10.0%

Most frequent character per script

ValueCountFrequency (%)
a127983
15.1%
r81204
 
9.6%
e73094
 
8.6%
s71948
 
8.5%
l61490
 
7.3%
t45583
 
5.4%
c45238
 
5.4%
i43851
 
5.2%
o41405
 
4.9%
n35168
 
4.2%
Other values (16)218380
25.8%
ValueCountFrequency (%)
_50384
53.8%
07714
 
8.2%
57696
 
8.2%
65887
 
6.3%
35878
 
6.3%
45862
 
6.3%
73529
 
3.8%
82275
 
2.4%
12083
 
2.2%
21232
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII938989
100.0%

Most frequent character per block

ValueCountFrequency (%)
a127983
13.6%
r81204
 
8.6%
e73094
 
7.8%
s71948
 
7.7%
l61490
 
6.5%
_50384
 
5.4%
t45583
 
4.9%
c45238
 
4.8%
i43851
 
4.7%
o41405
 
4.4%
Other values (27)296809
31.6%

productionDate
Real number (ℝ≥0)

Distinct69
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.585288
Minimum1904
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size586.4 KiB
2021-05-03T15:37:38.056199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1904
5-th percentile1999
Q12008
median2013
Q32016
95-th percentile2019
Maximum2020
Range116
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.175126381
Coefficient of variation (CV)0.003069781042
Kurtosis4.300844201
Mean2011.585288
Median Absolute Deviation (MAD)4
Skewness-1.406649563
Sum301898720
Variance38.13218582
MonotocityNot monotonic
2021-05-03T15:37:38.415061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201214733
 
9.8%
201312882
 
8.6%
201411870
 
7.9%
201710752
 
7.2%
200810301
 
6.9%
20189886
 
6.6%
20119772
 
6.5%
20198428
 
5.6%
20158179
 
5.4%
20107590
 
5.1%
Other values (59)45687
30.4%
ValueCountFrequency (%)
19041
< 0.1%
19361
< 0.1%
19372
< 0.1%
19382
< 0.1%
19391
< 0.1%
ValueCountFrequency (%)
20205855
3.9%
20198428
5.6%
20189886
6.6%
201710752
7.2%
20167254
4.8%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
автоматическая
83685 
механическая
27930 
роботизированная
19775 
вариатор
18690 

Length

Max length16
Median length14
Mean length13.14412313
Min length8

Characters and Unicode

Total characters1972670
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowвариатор
2nd rowавтоматическая
3rd rowвариатор
4th rowавтоматическая
5th rowмеханическая
ValueCountFrequency (%)
автоматическая83685
55.8%
механическая27930
 
18.6%
роботизированная19775
 
13.2%
вариатор18690
 
12.5%
2021-05-03T15:37:39.062253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:39.303416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
автоматическая83685
55.8%
механическая27930
 
18.6%
роботизированная19775
 
13.2%
вариатор18690
 
12.5%

Most occurring characters

ValueCountFrequency (%)
а383845
19.5%
т205835
10.4%
и169855
8.6%
о161700
8.2%
е139545
 
7.1%
я131390
 
6.7%
в122150
 
6.2%
м111615
 
5.7%
ч111615
 
5.7%
с111615
 
5.7%
Other values (6)323505
16.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1972670
100.0%

Most frequent character per category

ValueCountFrequency (%)
а383845
19.5%
т205835
10.4%
и169855
8.6%
о161700
8.2%
е139545
 
7.1%
я131390
 
6.7%
в122150
 
6.2%
м111615
 
5.7%
ч111615
 
5.7%
с111615
 
5.7%
Other values (6)323505
16.4%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic1972670
100.0%

Most frequent character per script

ValueCountFrequency (%)
а383845
19.5%
т205835
10.4%
и169855
8.6%
о161700
8.2%
е139545
 
7.1%
я131390
 
6.7%
в122150
 
6.2%
м111615
 
5.7%
ч111615
 
5.7%
с111615
 
5.7%
Other values (6)323505
16.4%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic1972670
100.0%

Most frequent character per block

ValueCountFrequency (%)
а383845
19.5%
т205835
10.4%
и169855
8.6%
о161700
8.2%
е139545
 
7.1%
я131390
 
6.7%
в122150
 
6.2%
м111615
 
5.7%
ч111615
 
5.7%
с111615
 
5.7%
Other values (6)323505
16.4%

owners
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
3 или более
58880 
1 владелец
49795 
2 владельца
41405 

Length

Max length11
Median length11
Mean length10.66821029
Min length10

Characters and Unicode

Total characters1601085
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3 или более
2nd row1 владелец
3rd row3 или более
4th row3 или более
5th row1 владелец
ValueCountFrequency (%)
3 или более58880
39.2%
1 владелец49795
33.2%
2 владельца41405
27.6%
2021-05-03T15:37:39.873849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:40.062738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
или58880
16.4%
более58880
16.4%
358880
16.4%
владелец49795
13.9%
149795
13.9%
владельца41405
11.5%
241405
11.5%

Most occurring characters

ValueCountFrequency (%)
л300160
18.7%
е258755
16.2%
а132605
8.3%
117760
 
7.4%
и117760
 
7.4%
 91200
 
5.7%
в91200
 
5.7%
д91200
 
5.7%
ц91200
 
5.7%
358880
 
3.7%
Other values (5)250365
15.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1242045
77.6%
Space Separator208960
 
13.1%
Decimal Number150080
 
9.4%

Most frequent character per category

ValueCountFrequency (%)
л300160
24.2%
е258755
20.8%
а132605
10.7%
и117760
 
9.5%
в91200
 
7.3%
д91200
 
7.3%
ц91200
 
7.3%
б58880
 
4.7%
о58880
 
4.7%
ь41405
 
3.3%
ValueCountFrequency (%)
358880
39.2%
149795
33.2%
241405
27.6%
ValueCountFrequency (%)
117760
56.4%
 91200
43.6%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic1242045
77.6%
Common359040
 
22.4%

Most frequent character per script

ValueCountFrequency (%)
л300160
24.2%
е258755
20.8%
а132605
10.7%
и117760
 
9.5%
в91200
 
7.3%
д91200
 
7.3%
ц91200
 
7.3%
б58880
 
4.7%
о58880
 
4.7%
ь41405
 
3.3%
ValueCountFrequency (%)
117760
32.8%
 91200
25.4%
358880
16.4%
149795
13.9%
241405
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic1242045
77.6%
ASCII267840
 
16.7%
None91200
 
5.7%

Most frequent character per block

ValueCountFrequency (%)
117760
44.0%
358880
22.0%
149795
18.6%
241405
 
15.5%
ValueCountFrequency (%)
л300160
24.2%
е258755
20.8%
а132605
10.7%
и117760
 
9.5%
в91200
 
7.3%
д91200
 
7.3%
ц91200
 
7.3%
б58880
 
4.7%
о58880
 
4.7%
ь41405
 
3.3%
ValueCountFrequency (%)
 91200
100.0%

pts
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Оригинал
136116 
Дубликат
13964 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1200640
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowДубликат
2nd rowОригинал
3rd rowДубликат
4th rowОригинал
5th rowДубликат
ValueCountFrequency (%)
Оригинал136116
90.7%
Дубликат13964
 
9.3%
2021-05-03T15:37:40.600085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:40.788357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
оригинал136116
90.7%
дубликат13964
 
9.3%

Most occurring characters

ValueCountFrequency (%)
и286196
23.8%
л150080
12.5%
а150080
12.5%
О136116
11.3%
р136116
11.3%
г136116
11.3%
н136116
11.3%
Д13964
 
1.2%
у13964
 
1.2%
б13964
 
1.2%
Other values (2)27928
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1050560
87.5%
Uppercase Letter150080
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
и286196
27.2%
л150080
14.3%
а150080
14.3%
р136116
13.0%
г136116
13.0%
н136116
13.0%
у13964
 
1.3%
б13964
 
1.3%
к13964
 
1.3%
т13964
 
1.3%
ValueCountFrequency (%)
О136116
90.7%
Д13964
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic1200640
100.0%

Most frequent character per script

ValueCountFrequency (%)
и286196
23.8%
л150080
12.5%
а150080
12.5%
О136116
11.3%
р136116
11.3%
г136116
11.3%
н136116
11.3%
Д13964
 
1.2%
у13964
 
1.2%
б13964
 
1.2%
Other values (2)27928
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic1200640
100.0%

Most frequent character per block

ValueCountFrequency (%)
и286196
23.8%
л150080
12.5%
а150080
12.5%
О136116
11.3%
р136116
11.3%
г136116
11.3%
н136116
11.3%
Д13964
 
1.2%
у13964
 
1.2%
б13964
 
1.2%
Other values (2)27928
 
2.3%

drive
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
полный
79614 
передний
59148 
задний
11318 

Length

Max length8
Median length6
Mean length6.788219616
Min length6

Characters and Unicode

Total characters1018776
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowполный
2nd rowзадний
3rd rowпередний
4th rowполный
5th rowполный
ValueCountFrequency (%)
полный79614
53.0%
передний59148
39.4%
задний11318
 
7.5%
2021-05-03T15:37:41.339788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:41.539240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
полный79614
53.0%
передний59148
39.4%
задний11318
 
7.5%

Most occurring characters

ValueCountFrequency (%)
н150080
14.7%
й150080
14.7%
п138762
13.6%
е118296
11.6%
о79614
7.8%
л79614
7.8%
ы79614
7.8%
д70466
6.9%
и70466
6.9%
р59148
 
5.8%
Other values (2)22636
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1018776
100.0%

Most frequent character per category

ValueCountFrequency (%)
н150080
14.7%
й150080
14.7%
п138762
13.6%
е118296
11.6%
о79614
7.8%
л79614
7.8%
ы79614
7.8%
д70466
6.9%
и70466
6.9%
р59148
 
5.8%
Other values (2)22636
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic1018776
100.0%

Most frequent character per script

ValueCountFrequency (%)
н150080
14.7%
й150080
14.7%
п138762
13.6%
е118296
11.6%
о79614
7.8%
л79614
7.8%
ы79614
7.8%
д70466
6.9%
и70466
6.9%
р59148
 
5.8%
Other values (2)22636
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic1018776
100.0%

Most frequent character per block

ValueCountFrequency (%)
н150080
14.7%
й150080
14.7%
п138762
13.6%
е118296
11.6%
о79614
7.8%
л79614
7.8%
ы79614
7.8%
д70466
6.9%
и70466
6.9%
р59148
 
5.8%
Other values (2)22636
 
2.2%

wheel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Левый
145294 
Правый
 
4786

Length

Max length6
Median length5
Mean length5.031889659
Min length5

Characters and Unicode

Total characters755186
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowЛевый
2nd rowПравый
3rd rowЛевый
4th rowЛевый
5th rowЛевый
ValueCountFrequency (%)
Левый145294
96.8%
Правый4786
 
3.2%
2021-05-03T15:37:42.090674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:42.287777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
левый145294
96.8%
правый4786
 
3.2%

Most occurring characters

ValueCountFrequency (%)
в150080
19.9%
ы150080
19.9%
й150080
19.9%
Л145294
19.2%
е145294
19.2%
П4786
 
0.6%
р4786
 
0.6%
а4786
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter605106
80.1%
Uppercase Letter150080
 
19.9%

Most frequent character per category

ValueCountFrequency (%)
в150080
24.8%
ы150080
24.8%
й150080
24.8%
е145294
24.0%
р4786
 
0.8%
а4786
 
0.8%
ValueCountFrequency (%)
Л145294
96.8%
П4786
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic755186
100.0%

Most frequent character per script

ValueCountFrequency (%)
в150080
19.9%
ы150080
19.9%
й150080
19.9%
Л145294
19.2%
е145294
19.2%
П4786
 
0.6%
р4786
 
0.6%
а4786
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic755186
100.0%

Most frequent character per block

ValueCountFrequency (%)
в150080
19.9%
ы150080
19.9%
й150080
19.9%
Л145294
19.2%
е145294
19.2%
П4786
 
0.6%
р4786
 
0.6%
а4786
 
0.6%

price
Real number (ℝ≥0)

ZEROS

Distinct4722
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1458735.765
Minimum0
Maximum25300000
Zeros34686
Zeros (%)23.1%
Memory size1.1 MiB
2021-05-03T15:37:42.515760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1246000
median897000
Q31800000
95-th percentile4990000
Maximum25300000
Range25300000
Interquartile range (IQR)1554000

Descriptive statistics

Standard deviation2274578.524
Coefficient of variation (CV)1.559280699
Kurtosis42.72939087
Mean1458735.765
Median Absolute Deviation (MAD)853000
Skewness5.402423374
Sum2.189270636 × 1011
Variance5.17370746 × 1012
MonotocityNot monotonic
2021-05-03T15:37:42.866250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034686
 
23.1%
9990001607
 
1.1%
12000001517
 
1.0%
18000001471
 
1.0%
17500001251
 
0.8%
7900001154
 
0.8%
15000001066
 
0.7%
5500001012
 
0.7%
600000913
 
0.6%
595000873
 
0.6%
Other values (4712)104530
69.6%
ValueCountFrequency (%)
034686
23.1%
200001
 
< 0.1%
280001
 
< 0.1%
300005
 
< 0.1%
330001
 
< 0.1%
ValueCountFrequency (%)
25300000338
0.2%
250000001
 
< 0.1%
247000001
 
< 0.1%
209900001
 
< 0.1%
209000001
 
< 0.1%

modelDate
Real number (ℝ≥0)

Distinct66
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.256243
Minimum1904
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size586.4 KiB
2021-05-03T15:37:43.580250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1904
5-th percentile1999
Q12007
median2008
Q32011
95-th percentile2016
Maximum2020
Range116
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.075921733
Coefficient of variation (CV)0.002527526928
Kurtosis10.66039996
Mean2008.256243
Median Absolute Deviation (MAD)3
Skewness-1.808526719
Sum301399097
Variance25.76498144
MonotocityNot monotonic
2021-05-03T15:37:43.940619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200828066
18.7%
200915687
10.5%
201114815
9.9%
201014213
9.5%
200710378
 
6.9%
200410092
 
6.7%
20127273
 
4.8%
20056471
 
4.3%
20065805
 
3.9%
20165636
 
3.8%
Other values (56)31644
21.1%
ValueCountFrequency (%)
19041
 
< 0.1%
19341
 
< 0.1%
19362
 
< 0.1%
19373
 
< 0.1%
19388
< 0.1%
ValueCountFrequency (%)
20205
 
< 0.1%
2019217
 
0.1%
2018867
 
0.6%
20172698
1.8%
20165636
3.8%

vendor
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
EUROPEAN
84026 
JAPANESE
66054 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1200640
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJAPANESE
2nd rowJAPANESE
3rd rowJAPANESE
4th rowJAPANESE
5th rowJAPANESE
ValueCountFrequency (%)
EUROPEAN84026
56.0%
JAPANESE66054
44.0%
2021-05-03T15:37:44.562210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:44.740521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
european84026
56.0%
japanese66054
44.0%

Most occurring characters

ValueCountFrequency (%)
E300160
25.0%
A216134
18.0%
P150080
12.5%
N150080
12.5%
U84026
 
7.0%
R84026
 
7.0%
O84026
 
7.0%
J66054
 
5.5%
S66054
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1200640
100.0%

Most frequent character per category

ValueCountFrequency (%)
E300160
25.0%
A216134
18.0%
P150080
12.5%
N150080
12.5%
U84026
 
7.0%
R84026
 
7.0%
O84026
 
7.0%
J66054
 
5.5%
S66054
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin1200640
100.0%

Most frequent character per script

ValueCountFrequency (%)
E300160
25.0%
A216134
18.0%
P150080
12.5%
N150080
12.5%
U84026
 
7.0%
R84026
 
7.0%
O84026
 
7.0%
J66054
 
5.5%
S66054
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1200640
100.0%

Most frequent character per block

ValueCountFrequency (%)
E300160
25.0%
A216134
18.0%
P150080
12.5%
N150080
12.5%
U84026
 
7.0%
R84026
 
7.0%
O84026
 
7.0%
J66054
 
5.5%
S66054
 
5.5%

test
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
115394 
1
34686 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0115394
76.9%
134686
 
23.1%
2021-05-03T15:37:45.256206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:45.447737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0115394
76.9%
134686
 
23.1%

Most occurring characters

ValueCountFrequency (%)
0115394
76.9%
134686
 
23.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
0115394
76.9%
134686
 
23.1%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
0115394
76.9%
134686
 
23.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
0115394
76.9%
134686
 
23.1%

options_abs
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
111372 
0
38708 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1
ValueCountFrequency (%)
1111372
74.2%
038708
 
25.8%
2021-05-03T15:37:45.942580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:46.117241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1111372
74.2%
038708
 
25.8%

Most occurring characters

ValueCountFrequency (%)
1111372
74.2%
038708
 
25.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
1111372
74.2%
038708
 
25.8%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
1111372
74.2%
038708
 
25.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
1111372
74.2%
038708
 
25.8%

options_lock
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
109102 
0
40978 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
1109102
72.7%
040978
 
27.3%
2021-05-03T15:37:46.634454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:46.807733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1109102
72.7%
040978
 
27.3%

Most occurring characters

ValueCountFrequency (%)
1109102
72.7%
040978
 
27.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
1109102
72.7%
040978
 
27.3%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
1109102
72.7%
040978
 
27.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
1109102
72.7%
040978
 
27.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
102835 
0
47245 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
1102835
68.5%
047245
31.5%
2021-05-03T15:37:47.291628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:47.465112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1102835
68.5%
047245
31.5%

Most occurring characters

ValueCountFrequency (%)
1102835
68.5%
047245
31.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
1102835
68.5%
047245
31.5%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
1102835
68.5%
047245
31.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
1102835
68.5%
047245
31.5%

options_computer
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
100341 
0
49739 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0
ValueCountFrequency (%)
1100341
66.9%
049739
33.1%
2021-05-03T15:37:47.935230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:48.125476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1100341
66.9%
049739
33.1%

Most occurring characters

ValueCountFrequency (%)
1100341
66.9%
049739
33.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
1100341
66.9%
049739
33.1%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
1100341
66.9%
049739
33.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
1100341
66.9%
049739
33.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
97748 
0
52332 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
197748
65.1%
052332
34.9%
2021-05-03T15:37:48.594096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:48.768994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
197748
65.1%
052332
34.9%

Most occurring characters

ValueCountFrequency (%)
197748
65.1%
052332
34.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
197748
65.1%
052332
34.9%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
197748
65.1%
052332
34.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
197748
65.1%
052332
34.9%

options_airbag-driver
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
95424 
0
54656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
195424
63.6%
054656
36.4%
2021-05-03T15:37:49.286808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:49.470706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
195424
63.6%
054656
36.4%

Most occurring characters

ValueCountFrequency (%)
195424
63.6%
054656
36.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
195424
63.6%
054656
36.4%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
195424
63.6%
054656
36.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
195424
63.6%
054656
36.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
95098 
0
54982 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
195098
63.4%
054982
36.6%
2021-05-03T15:37:49.995234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:50.170634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
195098
63.4%
054982
36.6%

Most occurring characters

ValueCountFrequency (%)
195098
63.4%
054982
36.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
195098
63.4%
054982
36.6%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
195098
63.4%
054982
36.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
195098
63.4%
054982
36.6%

options_airbag-passenger
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
91631 
0
58449 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
191631
61.1%
058449
38.9%
2021-05-03T15:37:50.658853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:50.853080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
191631
61.1%
058449
38.9%

Most occurring characters

ValueCountFrequency (%)
191631
61.1%
058449
38.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
191631
61.1%
058449
38.9%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
191631
61.1%
058449
38.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
191631
61.1%
058449
38.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
82136 
0
67944 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
182136
54.7%
067944
45.3%
2021-05-03T15:37:51.351723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:51.545219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
182136
54.7%
067944
45.3%

Most occurring characters

ValueCountFrequency (%)
182136
54.7%
067944
45.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
182136
54.7%
067944
45.3%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
182136
54.7%
067944
45.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
182136
54.7%
067944
45.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
80659 
0
69421 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
180659
53.7%
069421
46.3%
2021-05-03T15:37:52.008264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:52.198706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
180659
53.7%
069421
46.3%

Most occurring characters

ValueCountFrequency (%)
180659
53.7%
069421
46.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
180659
53.7%
069421
46.3%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
180659
53.7%
069421
46.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
180659
53.7%
069421
46.3%

options_esp
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
80508 
0
69572 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1
ValueCountFrequency (%)
180508
53.6%
069572
46.4%
2021-05-03T15:37:52.678243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:52.851622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
180508
53.6%
069572
46.4%

Most occurring characters

ValueCountFrequency (%)
180508
53.6%
069572
46.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
180508
53.6%
069572
46.4%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
180508
53.6%
069572
46.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
180508
53.6%
069572
46.4%

options_aux
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
76350 
0
73730 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1
ValueCountFrequency (%)
176350
50.9%
073730
49.1%
2021-05-03T15:37:53.319827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:53.495667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
176350
50.9%
073730
49.1%

Most occurring characters

ValueCountFrequency (%)
176350
50.9%
073730
49.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
176350
50.9%
073730
49.1%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
176350
50.9%
073730
49.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
176350
50.9%
073730
49.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
76116 
0
73964 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1
ValueCountFrequency (%)
176116
50.7%
073964
49.3%
2021-05-03T15:37:53.961871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:54.151977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
176116
50.7%
073964
49.3%

Most occurring characters

ValueCountFrequency (%)
176116
50.7%
073964
49.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
176116
50.7%
073964
49.3%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
176116
50.7%
073964
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
176116
50.7%
073964
49.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
75055 
0
75025 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1
ValueCountFrequency (%)
175055
50.0%
075025
50.0%
2021-05-03T15:37:54.634010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:54.828208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
175055
50.0%
075025
50.0%

Most occurring characters

ValueCountFrequency (%)
175055
50.0%
075025
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
175055
50.0%
075025
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
175055
50.0%
075025
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
175055
50.0%
075025
50.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
75637 
1
74443 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
075637
50.4%
174443
49.6%
2021-05-03T15:37:55.317606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:55.493943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
075637
50.4%
174443
49.6%

Most occurring characters

ValueCountFrequency (%)
075637
50.4%
174443
49.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
075637
50.4%
174443
49.6%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
075637
50.4%
174443
49.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
075637
50.4%
174443
49.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
75639 
1
74441 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
075639
50.4%
174441
49.6%
2021-05-03T15:37:55.987058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:56.177680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
075639
50.4%
174441
49.6%

Most occurring characters

ValueCountFrequency (%)
075639
50.4%
174441
49.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
075639
50.4%
174441
49.6%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
075639
50.4%
174441
49.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
075639
50.4%
174441
49.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
75884 
1
74196 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
075884
50.6%
174196
49.4%
2021-05-03T15:37:56.659630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:56.841642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
075884
50.6%
174196
49.4%

Most occurring characters

ValueCountFrequency (%)
075884
50.6%
174196
49.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
075884
50.6%
174196
49.4%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
075884
50.6%
174196
49.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
075884
50.6%
174196
49.4%

options_usb
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
76793 
1
73287 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
076793
51.2%
173287
48.8%
2021-05-03T15:37:57.331704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:57.520734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
076793
51.2%
173287
48.8%

Most occurring characters

ValueCountFrequency (%)
076793
51.2%
173287
48.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
076793
51.2%
173287
48.8%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
076793
51.2%
173287
48.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
076793
51.2%
173287
48.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
78508 
1
71572 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1
ValueCountFrequency (%)
078508
52.3%
171572
47.7%
2021-05-03T15:37:58.001272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:58.174748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
078508
52.3%
171572
47.7%

Most occurring characters

ValueCountFrequency (%)
078508
52.3%
171572
47.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
078508
52.3%
171572
47.7%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
078508
52.3%
171572
47.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
078508
52.3%
171572
47.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
79637 
1
70443 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150080
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
079637
53.1%
170443
46.9%
2021-05-03T15:37:59.083419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-03T15:37:59.256857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
079637
53.1%
170443
46.9%

Most occurring characters

ValueCountFrequency (%)
079637
53.1%
170443
46.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number150080
100.0%

Most frequent character per category

ValueCountFrequency (%)
079637
53.1%
170443
46.9%

Most occurring scripts

ValueCountFrequency (%)
Common150080
100.0%

Most frequent character per script

ValueCountFrequency (%)
079637
53.1%
170443
46.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII150080
100.0%

Most frequent character per block

ValueCountFrequency (%)
079637
53.1%
170443
46.9%

Interactions

2021-05-03T15:36:49.570674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:36:51.100272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:36:51.515545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:36:51.938283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:36:52.612809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:36:53.124352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:36:56.493415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:36:59.875304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:03.465729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:06.781737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:09.990990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:10.494201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:12.005692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:12.424901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:12.838822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:13.256917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:13.744881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:15.231836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:15.632111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:16.041362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:16.455692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:16.961135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:18.419278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:18.822291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:19.238098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:19.648780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:20.140152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:21.622630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:22.023354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-03T15:37:22.428399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-05-03T15:37:59.514923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-03T15:38:00.519976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-03T15:38:01.529909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-03T15:38:02.565974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-03T15:38:03.768332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-03T15:37:23.823341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-03T15:37:29.458075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexbodyTypebrandcolorengineDisplacementenginePowerfuelTypemileagemodel_nameproductionDatevehicleTransmissionownersptsdrivewheelpricemodelDatevendortestoptions_absoptions_lockoptions_electro-mirrorsoptions_computeroptions_front-seats-heatoptions_airbag-driveroptions_electro-window-frontoptions_airbag-passengeroptions_airbag-sideoptions_electro-window-backoptions_espoptions_auxoptions_wheel-leatheroptions_mirrors-heatoptions_multi-wheeloptions_light-sensoroptions_rain-sensoroptions_usboptions_front-centre-armrestoptions_cruise-control
00внедорожник 5 дв.MITSUBISHIбелый2.0146бензин112000outlander2012вариатор3 или болееДубликатполныйЛевый949000.02010JAPANESE011110101101001011001
11редкийMITSUBISHIбелый41.030электро78493minicab2014автоматическая1 владелецОригиналзаднийПравый600000.01999JAPANESE011010101000100000000
22хэтчбек 5 дв.MITSUBISHIзелёный1.8143бензин246525lancer2008вариатор3 или болееДубликатпереднийЛевый365000.02004JAPANESE001110111110000000000
33внедорожник 5 дв.MITSUBISHIчёрный3.0178бензин156000pajero2007автоматическая3 или болееОригиналполныйЛевый895000.02004JAPANESE011111111111111111011
44седанMITSUBISHIкрасный2.0265бензин1500lancer_evolution2005механическая1 владелецДубликатполныйЛевый2250000.02003JAPANESE011100111111111000010
55внедорожник 5 дв.MITSUBISHIчёрный2.4160бензин163000outlander2004автоматическая2 владельцаОригиналполныйЛевый444000.02010JAPANESE011111111110101000001
66внедорожник 5 дв.MITSUBISHIсиний1.6117бензин60000asx2014механическая1 владелецОригиналпереднийЛевый779000.02011JAPANESE001111000001011111000
77внедорожник 5 дв.MITSUBISHIчёрный3.8250бензин167203pajero2006автоматическая3 или болееДубликатполныйЛевый799000.02004JAPANESE011111111111110111111
88внедорожник 5 дв.MITSUBISHIсиний1.6117бензин8120asx2018механическая1 владелецОригиналпереднийЛевый1175000.02011JAPANESE011111101010001000000
99внедорожник 5 дв.MITSUBISHIсерый3.0170бензин112000pajero_sport2008автоматическая3 или болееОригиналполныйЛевый779000.02008JAPANESE011111111111101111100

Last rows

df_indexbodyTypebrandcolorengineDisplacementenginePowerfuelTypemileagemodel_nameproductionDatevehicleTransmissionownersptsdrivewheelpricemodelDatevendortestoptions_absoptions_lockoptions_electro-mirrorsoptions_computeroptions_front-seats-heatoptions_airbag-driveroptions_electro-window-frontoptions_airbag-passengeroptions_airbag-sideoptions_electro-window-backoptions_espoptions_auxoptions_wheel-leatheroptions_mirrors-heatoptions_multi-wheeloptions_light-sensoroptions_rain-sensoroptions_usboptions_front-centre-armrestoptions_cruise-control
15007034676внедорожник 5 дв.BMWсеребристый3.0218дизель370000x52003автоматическая3 или болееДубликатполныйЛевый0.02003EUROPEAN100000000000000000000
15007134677седанBMWчёрный1.9118бензин2126783er2001автоматическая3 или болееОригиналзаднийЛевый0.01998EUROPEAN111000111001000000010
15007234678седанBMWчёрный2.0245бензин1579655er2013автоматическая1 владелецОригиналполныйЛевый0.02013EUROPEAN111111111111111111110
15007334679внедорожник 5 дв.BMWсеребристый3.0235дизель315000x52008автоматическая1 владелецОригиналполныйЛевый0.02006EUROPEAN111111111111111111111
15007434680внедорожник 5 дв.BMWсеребристый3.0235дизель248000x52007автоматическая3 или болееДубликатполныйЛевый0.02006EUROPEAN111111111111110100011
15007534681седанBMWкоричневый1.6136бензин1150003er2014автоматическая3 или болееОригиналзаднийЛевый0.02011EUROPEAN111111111111111111011
15007634682седанBMWчёрный2.0190дизель980005er2018автоматическая1 владелецОригиналполныйЛевый0.02016EUROPEAN100000000000000000000
15007734683седанBMWсерый2.5170бензин3600005er1997автоматическая3 или болееДубликатзаднийЛевый0.01995EUROPEAN100000000000000000000
15007834684внедорожник 5 дв.BMWкоричневый2.0184дизель90500x12013автоматическая2 владельцаОригиналполныйЛевый0.02012EUROPEAN111111111111111111010
15007934685внедорожник 5 дв.BMWчёрный3.0235дизель240000x52008автоматическая3 или болееОригиналполныйЛевый0.02006EUROPEAN100000000000100000000